DNMF (version 1.4.2)

DNMF: Discriminant Non-Negative Matrix Factorization.

Description

Discriminant Non-Negative Matrix Factorization, DNMF, is to extend the Non-negative Matrix Factorization algorithm in order to extract features that enforce not only the spatial locality, but also the separability between classes in a discriminant manner.

Usage

DNMF(
  data,
  trainlabel,
  r = 2,
  gamma = 0.1,
  delta = 1e-04,
  maxIter = 1000,
  tol = 1e-07,
  log = TRUE,
  plotit = FALSE,
  checkH = TRUE,
  ...
)

Arguments

data

a matrix, like expression profilings of some samples. the columns are samples and the rows are gene's expression.

trainlabel

a numeric vector of sample type of all the samples, this vector should ONLY contain 1 and 2 so far and length of it should equal the column (sample) size of data.

r

the dimension of expected reduction dimension, with the default value 2.

gamma

the tradeoff value for the within scatter matrix, with the default value 0.1.

delta

the tradeoff value for the between scatter matrix, with the default value 1e-4.

maxIter

the maximum iteration of update rules, with the default value 1000.

tol

the toleration of coverange, with the default value 1e-7.

log

log2 data. Default is TRUE.

plotit

whether plot H (V=WH). Default: FALSE.

checkH

whether or not check H. Default: TRUE. This parameter aims to check whether or not the H safisfy the discriminant metagenes. Usually, this should be TRUE.

...

to gplots::heatmap.2

Details

The main algorithm is based on Zafeiriou, S., et al. (2006) Exploiting discriminant information in nonnegative matrix factorization with application to frontal face verification, IEEE transactions on neural networks, 17, 683-695, with some CORRECTIONs.

Examples

# NOT RUN {
dat <- rbind(matrix(c(rep(3, 16), rep(8, 24)), ncol=5), 
matrix(c(rep(5, 16), rep(5, 24)), ncol=5), 
matrix(c(rep(18, 16), rep(7, 24)), ncol=5)) + 
matrix(runif(120,-1,1), ncol=5)
trainlabel <- c(1,1,2,2,2)

DNMF_result <- DNMF(dat, trainlabel, r=2)


# }
# NOT RUN {
# Gene ranking. dat is the raw read count maatrix with sample in column.

#normalising dat
Sizefactors <- DESeq::estimateSizeFactorsForMatrix(dat)
dat = sweep(dat, 2, Sizefactors, `/`)

res <- DNMF(dat, trainlabel, r=2)
rnk <- res$rnk

#The end of gene ranking exmaples

#Other exmaples
DNMF_result <- DNMF(dat, trainlabel, r=2, gamma=0.1, delta=0.0001, plotit=TRUE)
# }
# NOT RUN {
# }